Method and System for Characterizing the Movement Speed of Particles Contained in a Liquid, such as Blood Particles

ABSTRACT

The method according to the invention is capable of characterizing a variation in the speed of particles or agglomeration of particles, the particles, such as blood particles, being contained in a liquid ( 12 ). 
     The characterization method includes the following steps:
         introducing the liquid ( 12 ) into a fluid chamber ( 14 );   lighting the fluid chamber ( 14 ) using an excitation laser beam ( 18 ) emitted by a light source ( 16 ), the laser beam ( 18 ) extending through the fluid chamber ( 14 ) in a longitudinal direction (X);   acquiring at least one image using a matrix photodetector ( 20 ), the image being formed by radiation transmitted by the lighted fluid chamber ( 14 ); and   calculating, from at least one acquired image, at least one indicator characterizing the variation of the speed or agglomeration of the particles.       

     During the acquisition step, the photodetector ( 20 ) is positioned at a distance (D2) smaller than 1 cm from the fluid chamber ( 14 ) in the longitudinal direction (X).

The present invention relates to a method for characterizing a variationin the speed of particles or agglomeration of particles, the particles,such as blood particles, being contained in a liquid, the methodincluding the following steps:

-   -   introducing the liquid into a fluid chamber;    -   lighting the fluid chamber using an excitation laser beam        emitted by a light source, the laser beam extending through the        fluid chamber in a longitudinal direction;    -   acquiring at least one image using a matrix photodetector, the        image being formed by radiation transmitted by the lighted fluid        chamber; and    -   calculating, from at least one acquired image, at least one        indicator characterizing the variation of the speed or        agglomeration of the particles.

The invention also relates to a system for characterizing the variationof the speed of particles or agglomeration of particles contained in theliquid, for example blood particles.

The invention in particular relates to the field of lenseless imaging ofthe laser beam lighting the fluid chamber, in order to characterize aliquid, such as blood.

The invention in particular applies to the determination of a parameterconcerning the coagulation of blood, in particular the measurement ofthe coagulation time. It also applies to the determination of aparameter regarding the agglutination of particles in the blood, inparticular the determination of the blood group by characterizing a cellaggregation between the blood to be tested and an antibody.

Known from document EP 2,233,923 A1 is a characterization method andsystem of the aforementioned type. The described method aims tocharacterize the coagulation or sedimentation dynamics of a fluidcontaining blood. The system for implementing this method comprises afluid chamber receiving liquid, a spatially coherent light sourcecapable of emitting a lighting laser beam and a mirror for reflectingthe laser beam toward the chamber. The laser beam extends in alongitudinal direction from the reflecting mirror toward the fluidchamber.

The system also comprises an image sensor, such as a matrix sensor ofthe CCD (Charged-Coupled Device) or CMOS (Complementary Metal OxideSemiconductor) type, arranged to make it possible to acquire a temporalseries of images of an optical granularity pattern created by theinteraction between the particles contained in the chamber and the laserbeam. The characterization system also comprises a processing unit forprocessing said temporal series of images.

The fluid chamber is positioned between the mirror and the image sensorin the longitudinal direction. The distance between the fluid chamberand the image sensor in the longitudinal direction is severalcentimeters or tens of centimeters. The laser beam emitted by thespatially coherent light source has a surface comprised between 10 μm²and several mm² along a plane perpendicular to the longitudinaldirection and passing through the fluid chamber.

Such a system and method make it possible to effectively characterizethe coagulation or sedimentation dynamics of the blood contained in theliquid.

However, such a system is relatively bulky. Furthermore, it makes itpossible to observe the coagulation phenomenon only in a relativelysmall volume of the fluid chamber.

The aim of the invention is therefore to propose a characterizationmethod and system making it possible to observe a larger volume ofliquid while limiting the bulk of the characterization system.

To that end, the invention relates to a characterization method of theaforementioned type, characterized in that, during the acquisition step,the photodetector is positioned at a distance smaller than 1 cm from thefluid chamber in the longitudinal direction.

According to other advantageous aspects of the invention, thecharacterization method comprises one or more of the following features,considered alone or according to any technically possible combinations:

-   -   the laser beam has a surface area comprised between 5 mm² and        200 mm², preferably equal to 25 mm², in a plane perpendicular to        the longitudinal direction, said plane being arranged in contact        with the fluid chamber;    -   the laser beam directly lights the fluid chamber, and the image        is formed directly by the radiation transmitted by the lighted        fluid chamber, in the absence of a magnifying lens positioned        between the fluid chamber and the photodetector; this does not        rule out the possible use of objective microlenses positioned at        each pixel of the photodetector;    -   during the acquisition step, several transmission images are        acquired sequentially at different moments, and during the        calculation step, a first calculated indicator is a correlation        indicator able to characterize the variation of the speed of the        particles, the correlation indicator being representative of the        correlation between at least two transmission images,        respectively acquired at moments n and n+m, or the correlation        for a predetermined region of said transmission images;    -   the method also includes a step for mixing the liquid with a        reagent capable of favoring slowing of the particles, such as a        reagent capable of favoring slowing of the blood particles by        means of coagulation of the blood;    -   the method also includes a step for determining, from the first        calculated indicator of the coagulation of blood particles        and/or from a time interval, called coagulation time, between an        initial moment and the moment at which the first calculated        indicator takes a predetermined value;    -   the light source is a spatially and temporally coherent light        source, such as a laser;    -   the first indicator is calculated from a correlation image        defining the spatial correlation between said transmission        images, or from a predetermined region of said correlation        image;    -   the correlation image is determined by the following equation:

${{Icorr}_{n + m}\left( {x,y} \right)} = \frac{\left( {{\left( {A_{n} \times A_{n + m}} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}{\sqrt{\left( {{\left( A_{n}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}\sqrt{\left( {{\left( A_{n + m}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}}$

where x and y represent the coordinates of a point of the image,Icorr_(n+m)(x,y) is a matrix having X rows and Y columns,

k1(x,y) represents a predetermined matrix having P rows and Q columns,

A_(n)(x,y) and A_(n+m)(x,y) are defined by the following equations:

A _(n)(x,y)=I _(n)(x,y)−(I _(n)

k1)(x,y)

A _(n+m)(x,y)=I _(n+m)(x,y)−(I _(n+m)

k1)(x,y)

I_(n)(x,y), I_(n+m)(x,y) representing two successive transmission imagesat moments n and n+m, I_(n)(x,y), I_(n+m)(x,y) being matrices with Xrows and Y columns,

and the symbol

represents the convolution integer defined by:

${\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\; {\sum\limits_{q = 0}^{Q}\; {{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}$

F(x,y) being a matrix with X rows and Y columns,

X, Y, P and Q being integers verifying X≧P≧1 and Y≧Q≧1;

-   -   the first indicator is calculated using the following equation:

${{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n + m}^{2}\left( {x,y} \right)}}}}$

where Ind1_(n,n+m) represents the first indicator,

C_(n)(x,y) and C_(n+m)(x,y) are defined by the following equations:

C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n)

C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m)

I′_(n)(x,y), I′_(n+m)(x,y) respectively represent a predetermined regionof two successive transmission images at moments n and n+m, x and ydesignating the coordinates of a point of the image, I′_(n)(x,y),I′_(n+m)(x,y) being matrices having N rows and M columns, and

I′_(n) , I′_(n+m) representing a mean value of respective predeterminedregions I′_(n)(x,y) and I′_(n+m)(x,y);

-   -   the method also includes a step for mixing the liquid with a        reagent capable of creating an agglomeration of particles, in        which, during the calculation step, a second calculated        indicator is an indicator for each acquired image, the second        indicator being representative of the intensity of the pixels of        the image in a predetermined region of the image and wherein the        method also includes a step for determining an agglomeration        state of the particles from the second calculated indicator;    -   the agglomeration state is determined when the second indicator        exceeds a predetermined threshold;    -   the agglomeration state is determined when the second indicator        exceeds a reference indicator, obtained by an image made in the        reference area;    -   the blood particles are red blood cells, the reagent includes an        antibody, and a piece of information relative to the blood group        is also determined from the agglomeration state;    -   the liquid includes an analyte, the method then including        estimating the quantity of said analyte in the liquid, depending        on said second indicator; and    -   the fluid chamber includes several fluid circulation channels,        and wherein, during the calculation step, an indicator is        calculated for each of the channels.

The invention also relates to a system for characterizing the variationof the speed of particles or the agglomeration of particles, theparticles, such as blood particles, being contained in the liquid, thesystem comprising:

-   -   a fluid chamber designed to receive the liquid;    -   a light source capable of emitting an excitation laser beam to        light the fluid chamber, the laser beam extending through the        fluid chamber in a longitudinal direction;    -   a matrix photodetector capable of acquiring at least one image,        of a radiation transmitted by the lighted fluid chamber; and    -   an information processing unit including calculation means for        calculating, from at least one acquired image, at least one        indicator characterizing the variation of the speed or the        agglomeration of the particles;

characterized in that the photodetector is positioned at a distancesmaller than 1 cm from the fluid chamber in the longitudinal direction.

According to another advantageous aspect of the invention, the matrixphotodetector includes a plurality of pixels, each pixel havingdimensions each smaller than or equal to 4 μm.

These features and advantages of the invention will appear upon readingthe following description, provided solely as a non-limiting example,and done in reference to the appended drawings, in which:

FIG. 1 is a very diagrammatic illustration of a characterization systemaccording to the invention, comprising a fluid chamber receiving liquidto be characterized, a light source capable of lighting the chamber in alongitudinal direction, a matrix photodetector for acquiring images ofthe radiation transmitted by the lighted chamber and an informationprocessing unit,

FIG. 2 is a very diagrammatic illustration of the characterizationsystem according to the invention, according to another arrangement ofthe light source relative to the matrix photodetector,

FIG. 3 is a diagrammatic view of the fluid chamber in the longitudinaldirection, as well as the arrangement of the matrix photodetector ofFIG. 1 relative to the chamber, according to a first alternative,

FIG. 4 is a view similar to that of FIG. 3 according to a secondalternative,

FIG. 5 is a flowchart of a characterization method according to theinvention,

FIG. 6 is an image of an empty channel of the chamber of FIG. 1,acquired by the matrix photodetector,

FIGS. 7 and 8 are images of the chamber containing the liquid, acquiredby the photodetector at different moments,

FIGS. 9 and 10 are correlation images, calculated by the processing unitof FIG. 1, from images acquired at different moments,

FIG. 11 is a depiction of the evolution over time of an indicatorcharacterizing a variation of the speed of the particles contained inthe liquid, such as a slowing of the particles,

FIG. 12 is a table illustrating the case of cell aggregations as afunction of the blood group and the antibody deposited,

FIG. 13 is a view similar to that of FIGS. 3 and 4 according to a secondembodiment, the fluid chamber including two channels,

FIGS. 14 and 15 are respective images of the first and second channelsof the chamber of FIG. 13, the second channel having a cell aggregation,the images being acquired by the photodetector of FIG. 1,

FIGS. 16 and 17 are histograms of the gray level of the images acquiredin FIGS. 14 and 15,

FIGS. 18 to 21 are images of the liquid to be characterized, acquired bythe photodetector according to a second example of the secondembodiment, the liquid to be characterized containing blood, to which avariable quantity of antibodies is added, these images being acquiredfor increasing quantities of antibodies,

FIGS. 22 to 25 are histograms of the gray level of the images acquiredin FIGS. 18 to 21, respectively,

FIGS. 26 to 29 are images of the liquid characterized in FIGS. 18 to 21,respectively, obtained, after dilution, using a microscope and formingreference images,

FIGS. 30 to 34 are images of the liquid to be characterized, acquired bythe photodetector according to a third example of the second embodiment,the liquid to be characterized containing blood, a variable quantity ofA protein, and a same added quantity of antibodies,

FIGS. 35 to 39 are histograms of the gray level of the images acquiredin FIGS. 30 to 34, respectively,

FIGS. 40 to 44 are images of the liquid characterized in FIGS. 30 to 34,respectively, obtained after dilution using a microscope and formingreference images, and

FIG. 45 is a very diagrammatic illustration of the agglutination of thered blood cells and A proteins using antibodies.

In FIG. 1, a characterization system 10 is designed to characterize avariation of the speed of particles or the agglomeration of particles,the particles, such as blood particles, being contained in a liquid 12,through the acquisition of images formed by a radiation transmitted bythe lighted liquid 12, then processing those images. The variation ofthe speed of particles is, for example, a slowing of the particles aswill be described in more detail hereafter. One skilled in the art willunderstand that the characterization system 10 according to theinvention is similarly capable of characterizing the acceleration of theparticles.

Thus, in general, the characterization system 10 is designed tocharacterize a parameter of a liquid comprising particles, that liquidin particular being blood. This parameter is, for example, a coagulationor an agglomeration of particles making up the liquid. Alternatively, itis a count of the particles or an observation of the morphology of theparticles.

The term “particles” in particular refers to a biological particle,i.e., a cell (for example, a red blood cell, a white blood cell, or aplatelet), a bacteria, a virus, or any other molecule (for example, aprotein).

Agglutination (or agglomeration) refers to the formation of athree-dimensional structure of particles connected to each other, underthe effect of a reagent that has been introduced.

Agglutination (or agglomeration) state refers to an estimate, which maybe relative or absolute, of the size of the agglutinates or relative tothe quantity of particles present in the agglutinates.

The characterization system 10 comprises a fluid chamber 14 designed toreceive the liquid 12, a light source 16 capable of emitting anexcitation laser beam 18 to light the fluid chamber 14, the laser beam18 oriented in a longitudinal direction X through the fluid chamber 14,and a matrix photodetector 20 capable of acquiring images of theradiation transmitted by the fluid chamber 14 lighted by the laser beam18. Transmitted radiation refers to the radiation passing through thefluid chamber, such that the matrix photodetector 20 and the lightsource 16 are situated on either side of the fluid chamber 14.

The characterization system 10 comprises an information processing unit21 and a screen 22 for displaying an image of the chamber 14.

In the described embodiment, the characterization system 10 is capableof characterizing the coagulation of the blood or the agglutination ofblood particles, the agglutination of blood particles making it possibleto determine the associated blood group. The liquid 12 then containsblood. The liquid 12 is, for example, whole blood, a fraction of theblood, or a blood plasma. Alternatively, the liquid 12 is another bodilyfluid, such as urine, perspiration, etc.

The fluid chamber 14 is positioned between the light source 16 and thematrix photodetector 20 in the longitudinal direction X. The fluidchamber 14 comprises a deposition area 26 of the liquid and one or morecirculation channels 28 for the liquid 12, as shown in FIG. 3.

The fluid chamber 14 includes at least one fluid channel, delimited, indirection X, by an upper plate and a lower plate, not shown. Theseplates are at least partially translucent so as to make it possible tolight the liquid 12 using the light source 16, as well as to detect theradiation transmitted by the matrix detector 20.

The lower and upper plates are, for example, glass slides, not shown,and separated by spacers, not shown, such that the glass slides areseparated by approximately 160 μm in the longitudinal direction X.

The fluid chamber 14 has a thickness E in the longitudinal direction X.The thickness E for example has a value comprised between 20 μm and 1000μm, preferably comprised between 30 μm and 300 μm.

The light source 16 is capable of emitting the laser beam 18 in thelongitudinal direction X.

The light source 16 is positioned at a first distance D1 from the fluidchamber 14 in the longitudinal direction X. The first distance D1preferably has a value comprised between 1 cm and 30 cm, for exampleequal to 20 cm.

In the described embodiment, the light source 16 is a spatially andtemporally coherent source. The light source 16 is, for example, alaser. Alternatively, the light source 16 is a laser diode (LD) or alaser diode of the VCSEL (Vertical Cavity Surface Emitting Laser) type.

Also alternatively, the light source 16 is a light-emitting diode (LED),monochromatic and having small enough dimensions to be consideredspatially coherent, the diameter of the LED being smaller than 1/10 ofthe first distance D1 separating that LED from the chamber.

The laser beam 18, oriented in the longitudinal direction X, has, at thelevel of the fluid chamber, a surface area comprised between 5 mm² and200 mm², preferably equal to 25 mm², in a plane P perpendicular to thelongitudinal direction X, as shown in FIG. 1. The plane P is arranged incontact with the fluid chamber 14. Thus, the lighted fluid surface islarger than in the state of the art. This makes it possible to eliminatelocal fluctuations of the parameter that one wishes to determine.

The laser beam 18 is capable of lighting the fluid chamber 14 directly,preferably in the absence of a magnification lens positioned between thelight source 16 and the fluid chamber 14.

The matrix photodetector 20 is a pixelated image sensor, including aplurality of pixels, not shown. Each pixel of the photodetector 20 hasdimensions smaller than or equal to 10 μm, or even 4 μm. Each pixel is,for example, square, each side having a value smaller than or equal to10 μm, or even 4 μm. In the described embodiment, each pixel is in theform of a square with sides measuring 4 μm. Alternatively, each pixel isin the form of a square with each side measuring 2.2 μm.

The matrix photodetector 20 is positioned at a second distance D2 fromthe fluid chamber 14 in the longitudinal direction X. The seconddistance D2 has a value smaller than 1 cm, and preferably comprisedbetween 100 μm and 2 mm. Favoring a short distance between the detectorand the chamber makes it possible to limit the interference phenomenabetween the different diffraction patterns. In fact, when this distanceincreases, these interferences can make the image unusable, inparticular when the number of diffracting particles increases. This isdue to the fact that the volume of fluid that is lighted is greater thanin the device described in application EP 2,233,923 A1 of the state ofthe art. By placing the detector at a distance of more than 1 cm away,the image obtained on the detector would be difficult to use.

The images acquired by the matrix photodetector 20 are formed by theradiation transmitted directly by the lighted fluid chamber 14, in theabsence of a magnification lens positioned between the fluid chamber 14and the matrix photodetector 20. The matrix photodetector 20 is alsocalled a lenseless imaging device, and is capable of forming an image ofthe fluid chamber 14 while being placed at a small distance therefrom. Asmall distance refers to a distance smaller than 1 cm.

The matrix photodetector 20 is capable of generating at least one imageevery 5 seconds, and the acquisition rhythm is therefore greater than0.2 Hz. The matrix photodetector 20 is a two-dimensional image sensor,i.e., in a plane perpendicular to the longitudinal axis X. Theacquisition frequency of the images is preferably comprised between 1 Hzand 20 Hz.

The matrix photodetector 20 is for example a CCD sensor. Alternatively,the photodetector 20 is a CMOS sensor.

The matrix photodetector 20 is for example substantially aligned withthe fluid chamber 14 in the longitudinal direction X, as illustrated inFIG. 3, where the photodetector 20 is shown in dotted lines.

Alternatively, the matrix photodetector 20 is slightly offset relativeto the chamber 14 along the longitudinal axis X, as illustrated in FIG.4, where the photodetector 20 is also shown in dotted lines.

The information processing unit 21, shown in FIG. 1, includes a dataprocessor 30 and a memory 32 associated with the processor.

In the example embodiment of FIG. 2, the matrix photodetector 20, thelight source 16, and optionally all or part of the informationprocessing unit 21 are secured to a second substrate 23. Thecharacterization system 10 comprises an optical system 24, for example amirror, making it possible to return the laser beam 18 from the lightsource 16 toward the photodetector 20. This makes it possible to have acompact system. The fluid chamber 14 is for example formed in aremovable support 25. The removable support 25 is for example disposableand designed to be inserted overhanging the photodetector 20, at a smalldistance therefrom, such that the fluid chamber can be lighted by thelaser beam 18. According to this example embodiment, the support 25 isdesigned to receive the fluid to be analyzed 12, then to be insertednear the photodetector 20 so that the analysis can be done. It forexample includes a conduit, in which the fluid 12 circulates as far asthe channel 28 of the fluid chamber, the fluid chamber 14 beingconnected to that conduit. When the analysis is complete, the support 25is removed, in particular to be thrown away. The characterization system10 is then available to perform another measurement with anothersupport.

One skilled in the art will understand that, in the example embodimentof FIG. 2, the longitudinal direction X corresponds to the last portionof the laser beam 18 between the corresponding mirror of the opticalsystem 24 and the photodetector 20, passing through the fluid chamber14.

The or each circulation channel 28 has a width L, shown in FIGS. 3 and4. The width L for example has a value comprised between 50 μm and 5 mm,preferably equal to 1.5 mm.

The memory 32 is capable of storing software 34 for receiving the imagesacquired by the matrix photodetector 20, first software 36 forcalculating a first indicator Ind1_(n,n+m) capable of characterizing thedesired parameter; in this case, the variation of the speed of theparticles, such as their slowing. Additionally or alternatively, thememory 32 can store second software 38 for calculating a secondindicator Ind2 capable of characterizing another desired parameter, inthis case the agglomeration of the particles. The memory 32 is alsocapable of storing software 40 for characterizing the variation of thespeed of the particles and/or the agglomeration of the particles.

Alternatively, the reception means 34, the first calculation means 36,the second calculation means 38 and the characterization means 40 aremade in the form of programmable logic components or in the form ofdedicated integrated circuits.

The reception software 34 is capable of regularly receiving, from thephotodetector 20, the images acquired sequentially at different moments.The reception software 34 is capable of receiving at least one image persecond, and the reception rhythm of the images is greater than 0.2 Hz,typically from 1 Hz to 20 Hz.

The first calculation software 36 is capable of calculating an imageA_(n), representing the transmission image I_(n)(x,y), from which alocal mean is taken out. The latter is obtained by convoluting the imageI_(n)(x,y) with a kernel k1. This kernel k1 is a matrix with smalldimensions relative to I_(n). For example, the dimensions of the kernelk1 are 10 pixels by 10 pixels, and the dimensions of I_(n) are at leasttwice as large as those of the kernel k1, or even 10 times larger. Thekernel k1, including P rows and Q columns, is for example homogenous,all of its values being identical. According to the preceding, P and Qare integers, for example equal to 10. Thus, two images A_(n) andA_(n+m) are established, respectively corresponding to the moments n andn+m, m being an integer. In general, m is equal to 1, the transmissionimages I_(n), and I_(n+1) being two successive transmission images.

A _(n)(x,y)=I _(n)(x,y)−(I _(n)

k1)(x,y)  (1)

A _(n+m)(x,y)=I _(n+m)(x,y)−(I _(n+m)

k1)(x,y)  (2)

where I_(n)(x, y), I_(n+m)(x, y) represent two successive transmissionimages at moments n and n+m, x and y representing the coordinates of apoint of the respective image, I_(n)(x,y), I_(n+m)(x,y) being matriceshaving X rows and Y columns, the symbol

representing the convolution integer defined by the following equation:

$\begin{matrix}{{\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\; {\sum\limits_{q = 0}^{Q}\; {{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}} & (3)\end{matrix}$

F being a matrix with X rows and Y columns,

k1 representing a kernel for the correlation of the acquired images, k1being a matrix with P rows and Q columns,

X, Y, P and Q being integers verifying X≧P≧1 and Y≧Q≧1.

The images are for example acquired every second by the matrixphotodetector 20, and the two transmission images I_(n)(x,y),I_(n+1)(x,y) are then images acquired with an interval of one second.

The first calculation software 36 is then capable of calculating acorrelation image Icorr_(n,n+m)(x,y) representative of the correlationbetween two transmission images I_(n)(x,y), I_(n+m)(x,y) for exampleaccording to the following equation:

$\begin{matrix}{{{Icorr}_{n,{n + m}}\left( {x,y} \right)} = \frac{\left( {{\left( {A_{n} \times A_{n + m}} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}{\sqrt{\left( {{\left( A_{n}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}\sqrt{\left( {{\left( A_{n + m}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}}} & (4)\end{matrix}$

where Icorr_(n,n+m)(x,y) represents the correlation image of twotransmission images I_(n), I_(n+m), established at respective moments nand n+m; x and y represent the coordinates of a point of the image,Icorr_(n,n+m)(x,y) being a matrix with X rows and Y columns.

The first calculation software 36 is lastly capable of calculating thefirst indicator Ind1_(n,n+m) from the correlation imageIcorr_(n,n+m)(x,y) previously obtained. This indicator Ind1_(n,n+m) isrepresentative of the intensity of the image Icorr_(n,n+m)(x,y). Thisindicator Ind1_(n,n+m) is then capable of characterizing the variationof the speed of the particles, such as their slowing.

The correlation indicator Ind1_(n,n+m) is representative of thecorrelation between at least two transmission images I_(n)(x,y) andI_(n+m)(x,y) respectively acquired at moments n and n+m, thatcorrelation being established for a region of interest 142 of thecorrelation image Icorr_(n,n+m)(x,y). Said region of interest 142 isdetermined by the user. It corresponds to the area of the correlationimage Icorr_(n,n+m)(x,y) that one wishes to use to determine thecorrelation indicator Ind1_(n,n+m). It is for example a square areahaving several dozen pixels per side, for example 50×50 pixels. Thecorrelation indicator Ind1_(n,n+m) translates the value of the intensityin that region of interest 142. It is in particular determined from themean intensity or the total intensity in the region of interest 142 ofthe image Icorr_(n,n+m)(x,y). That indicator Ind1_(n,n+m) for examplerepresents the mean intensity level or said total intensity in theregion of interest 142.

Alternatively, the first calculation software 36 is capable ofcalculating intermediate images C_(n)(x,y), C_(n+m)(x,y) from twotransmission images I_(n)(x,y), I_(n+m)(x,y) acquired at moments n andn+m, according to the following equation:

C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n)   (5)

C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m)   (6)

where I′_(n)(x,y), I′_(n+m)(x,y) respectively represent a region ofinterest of the two transmission images I_(n) and I_(n+m). As previouslystated, the index m is for example equal to 1. The coordinates x and ydesignate the coordinates of a point of the image, C_(n)(x,y),C_(n+m)(x,y) being matrices having N rows and M columns, and

I′_(n) , I′_(m) representing a mean value of the respective regions ofinterest I_(n′)(x,y), I_(m′)(x,y). We must specify that the subtractionof I′_(n) and I′_(m) by I′_(n) and I′_(m) , respectively, is optional.This corresponds to a normalization step, making it possible to obtainan indicator comprised between 0 and 1. Furthermore, this makes itpossible to eliminate the fluctuation effect of the lighting intensityof the medium between two images.

According to this alternative, the first calculation software 36 is thencapable of calculating the first indicator Ind1_(n,n+m) according to thefollowing equation:

$\begin{matrix}{{{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n + m}^{2}\left( {x,y} \right)}}}}} & (7)\end{matrix}$

where Ind1_(n,n+m) represents the first indicator.

The characterization software 40 is capable of characterizing thevariation of the speed and/or agglomeration of particles contained inthe liquid 12. More specifically, the characterization software 40 iscapable of determining, from the first calculated indicatorInd1_(n,n+m), the variation of the speed of the particles contained inthe liquid 12, such as their slowing. In the embodiment described wherethe liquid 12 contains blood, the first characterization software 40 iscapable of determining, from the first calculated indicatorInd1_(n,n+m), the coagulation of the blood particles and/or a timeinterval, called coagulation time, between an initial moment and themoment when the first calculated indicator Ind1_(n,n+m) takes apredetermined value. Thus, in general, Ind1_(n,n+m) characterizes acoagulation parameter of the blood, based on the observation oftransmission images I_(n), I_(n)+m at moments n and n+m, m generallybeing comprised between 1 and 10, and preferably equal to 1.

The operation of the characterization system 10 according to theinvention will now be described using FIG. 5, showing a flowchart of thecharacterization method according to the invention.

Before use thereof, the circulation channel(s) 28 of the fluid chamberare empty, and an initial image I₀ of the chamber 14 then shows a whitearea corresponding to the circulation channel 28 and areas delimitingthe channel, in this example appearing in the form of dark areascorresponding to the rest of the fluid chamber 14, as shown in FIG. 6.

During the initial step 100, the liquid 12 is introduced into thedeposition area 26 of the fluid chamber. The liquid 12 flows bycapillarity in the deposition area 26 toward the circulation channel(s)28.

The liquid 12 is then optionally, in step 110, mixed with a reagent 112,shown in FIGS. 3 and 4, and capable of triggering or favoring a slowingphenomenon of the particles. The reagent 112 is, for example, alyophilized reagent capable of favoring the slowing of the bloodparticles through coagulation of the blood.

The reagent 112 is, for example, deposited upstream from the opticaldetection area corresponding to the area inside the dotted lines in FIG.3 for which an image is acquired by the photodetector 20. Alternatively,the reagent 112 is positioned inside the optical detection area, asshown in FIG. 4. The mixing between the liquid 12 and the reagent 112occurs when the liquid 12 flows in contact with the reagent 112 insidethe circulation channel 28 (arrow F1).

In the described embodiment, the reagent 112 is a pro-coagulant protein.This protein is deposited, dried or lyophilized in the circulationchannel 28. The reagent 112 is for example the prothrombin protein, alsocalled PT, when the INR (International Normalized Ratio) parameter isbeing determined.

${INR} = \left( \frac{T}{Tref} \right)^{ISI}$

T being the measured coagulation time, Tref being the consideredreference time, ISI being a correction factor that depends on thereagents used to trigger the coagulation.

Alternatively, the reagent 112 is the Ecarin protein, when thecoagulation time is measured using the ECT (Ecarin Clotting Time) test.Alternatively, the reagent 112 is the Thrombin protein when thecoagulation time is measured using the TT (Thrombin Time) test.

The liquid 12 is lighted by the laser beam 18 during the step 120. Thelight source 16 in fact emits the laser beam 18 toward the fluid chamber14 in which the liquid 12 is found in the longitudinal direction X.

During the step 130, the matrix photodetector 20 then sequentiallyacquires several transmission images I_(n)(x,y), I_(n+m)(x,y) atdifferent moments n and n+m. Each transmission image I_(n)(x,y), I_(n+m)(x,y) is formed by the transmitted radiation, and the correspondingacquisition moment, by the lighted fluid chamber 14.

The images I_(n)(x,y), I_(n+m)(x,y) are for example immediatelysuccessive images, m then being equal to 1, preferably acquired everysecond, as shown in FIGS. 7 and 8, where the time gap between the twoimages I_(n)(x,y), I_(n+1)(x,y) successively acquired is equal to 1second.

The acquired images I_(n)(x,y), I_(n+1)(x,y) correspond to theinterferences of diffraction patterns created by particles suspended inthe liquid 12. The lighting of the particles by the spatially andtemporally coherent beam 18, such as a laser beam, creates a diffractionpattern, which varies over time due to the movement of particlescontained in the liquid 12.

The observation of a usable diffraction pattern, by placing the matrixphotodetector 20 at such a small distance away, is in particular due tothe absence of a magnification lens between the fluid chamber 14 and thephotodetector 20.

During the acquisition step 130, the photodetector 20 is positioned at asmall distance from the fluid chamber 14, the second distance D2 betweenthe fluid chamber 14 and the photodetector 20 in the longitudinaldirection X being smaller than 1 cm.

At the end of the acquisition step 130, in particular after theacquisition of the images I_(n)(x,y), I_(n+1)(x,y), the firstcalculation software 36 begins, in the step 140, by calculating theimages A_(n)(x,y), A_(n+1)(x,y) using equations (1), (2) and (3).

The first calculation software 36 then calculates the correspondingcorrelation image Icorr_(n,n+m)(x,y) from the images A_(n)(x,y),A_(n+1)(x,y) and using equation (4).

In the described embodiment, the correlation images evolve as a functionof time, as shown in FIGS. 9 and 10, where the correlation imageIcorr_(N1,N1+1)(x,y), denoted Icorr_(N1)(x,y), corresponding tocorrelation between two transmission images I_(n), I_(n+1) done withn=N1 before the coagulation (FIG. 9), has a very different appearancefrom the image Icorr_(N2)(x,y) corresponding to a correlation betweentwo transmission images I_(N2), I_(N2+1), done with n=N2 after thecoagulation (FIG. 10).

The first calculation software 36 lastly calculates the value of thefirst indicator Ind1_(n,n+1) for each correlation imageIcorr_(n,n+1)(x,y) obtained. The value of the first indicatorInd1_(n,n+1) is for example the mean value of the points of thecorrelation image Icorr_(n,n+1)(x,y) in the predetermined region ofinterest 142, visible in FIGS. 9 and 10. Alternatively, the value of thefirst indicator Ind1_(n,n+1) is the integral of the image in the regionof interest 142 (i.e., the sum of the gray levels of each pixel). Alsoalternatively, the value of the first indicator Ind1_(n,n+1) is afunction of that integral.

Alternatively, the first calculation software 36 begins, in step 140, bycalculating the intermediate images C_(n)(x,y), C_(n+1)(x,y) usingequations (5) and (6).

The first calculation software 36 then populates the value of the firstindicator Ind1_(n,n+1) from intermediate images C_(n)(x,y), C_(n+1)(x,y)and using equation (7).

In the described embodiment, at the end of the calculation step 140, thecharacterization method returns to step 130 in order to acquire a newimage of the lighted fluidchamber 14, then to calculate, similarly toduring step 140, a new correlation image Icorr_(n+1,n+2)(x,y) and a newvalue of the first indicator Ind1_(n+1,n+2).

The acquisition 130 and calculation 140 steps are then reiteratedregularly, for example every second, for a predetermined length of time,for example longer than 60 seconds, or until a stop initiated by theuser, in particular in light of the evolution over time of the firstindicator.

The evolution over time of the first indicator Ind1_(n,n+m), shown inthe embodiment described by the curve 145 shown in FIG. 11, then makesit possible to calculate the variation of the speed of the particlescontained in the liquid 12, such as the slowing of the blood particles.

In FIG. 11, an initial moment t0 corresponds to the mixture of thereagent 112 with the liquid 12, and the circulation channel 28 acrossfrom the optical detection area is then filled at a first moment t1. Inother words, the example embodiment of FIG. 11 corresponds to the casewhere the reagent 112 is deposited just before the optical detectionarea, as shown in FIG. 3.

The curve 145 then shows, from the first moment t1, a decrease in thevalue of the first indicator Ind1_(n,n+m) to reach a minimum value ofless than 0.1. The curve 145 then shows, from the second moment t2_(A),a rapid increase in the value of the first indicator Ind1_(n,n+m) untilthat value stabilizes around 0.8.

The phase between the first and second moments t1, t2_(A), also calledfirst phase, during which the value of the first indicator Ind1_(n,n+m)is low, corresponds to a low correlation between the transmission imagessuccessively acquired. In fact, this is due to a significant change inthe diffraction pattern from one image to the other during the firstphase, due to the movement of the particles suspended in a liquid 12, inthe space corresponding to the region of interest 142.

The phase beginning at the second moment t2_(A) and until the end of thecharacterization, also called second phase, corresponds to a swelling ofthe particles in a liquid 12, which amounts to an increase in thecorrelation between the successive images.

The characterization software 40 then determines, from the firstpopulated indicator Ind1_(n,n+m), the time interval between the initialmoment t0 and the moment t2_(A) at which the first indicator again hasincreasing correlation values. The time interval between the originalmoment t0 and the second moment t2_(A) is also called coagulation timeTc. In the example embodiment of FIG. 11, the coagulation time Tc isapproximately equal to 29 s.

The characterization software 40 also determines the coagulation ofblood particles from the first populated indicator Ind1_(n,n+m). Thecoagulation time T for example corresponds to the time gap between theinitial moment t0 (t=0 on curve 145 in FIG. 11) and the coagulationmoment, which characterizes the coagulation of blood.

This coagulation moment for example corresponds to a point where thecurve 145 reaches a plateau, beyond which the values of the firstindicator Ind1_(n,n+m) practically no longer evolve (second momentt2_(A)). It will then be understood that this coagulation moment t2_(A)can also be determined from the derivative of the time functiondescribing the evolution of the first indicator Ind1_(n,n+m), forexample when the latter drops below a certain threshold.

Alternatively, this regulation moment is determined from the secondderivative of that function. From that second derivative, it is possibleto situate an inflection point 146 of the curve 145. The moment t2_(A)corresponding to that inflection point 146 is then for example used todetermine the coagulation moment.

In the example embodiment of FIG. 11, a tangent 147 to the curve 145 isdrawn, passing through said inflection point 146. The coagulation momentthen corresponds to the abscissa at which said tangent 147 crosses thebaseline of said curve (second moment t2_(A)) or the abscissa at whichsaid tangent 147 crosses the x-axis (third moment t2_(B)). A baselinerefers to the portion of the curve, substantially planar, preceding thesharp increase in the value of the first indicator Ind1_(n,n+m). In theillustrated example, the baseline corresponds to the portion of thecurve 145 whereof the abscissa are comprised between approximately 15seconds and 28 seconds (second moment t2_(A)).

As previously indicated, the coagulation time T is obtained by adifference between the coagulation moment t2_(A), t2_(B) and the initialmoment t0, chosen at the origin of the x-axis of the curve 145.

The characterization system 10 and the characterization method accordingto the invention therefore make it possible to characterize thevariation of the speed of the particles contained in the liquid 12 overa large portion of the circulation channel(s) 28 of the fluid chamberdue to the small distance between the fluid chamber 14 and thephotodetector 20.

In the example embodiment of FIG. 11, the characterization system 10 andthe characterization method according to the invention make it possibleto characterize a slowing of the particles by detecting an increase inthe correlation between the successive images.

One skilled in the art will understand that the characterization system10 and the characterization method according to the invention make itpossible, similarly, to characterize an acceleration of the particles bydetecting a decrease in the correlation between the successive images.

The second distance D2 smaller than 1 cm between the fluid chamber 14and the photodetector 20 also makes it possible to limit the bulk of thecharacterization system 10.

Further, the significant scope of the laser beam 18 along the plane P,i.e., greater than 5 mm², and for example comprised between 5 mm² and200 mm², makes it possible to limit the heating of the liquid 12contained in the fluid chamber 14. In fact, the significant surface areaof the laser beam 18 makes it possible to have an optical density with alow power.

Furthermore, using an extended laser beam and forming an image of thesmall distance from the chamber makes it possible to examine an evenlarger volume of fluid. The influence of local phenomena capable ofbecoming predominant when the laser beam is finer is then eliminated,and the volume of analyzed fluid is practically unique. Analyzing thecorrelation between two transmission images I_(n), I_(n+m) makes itpossible to take into account the spatial structure of the coagulation,in the plane of the microfluidic channel 28. In other words, theevolution of the coagulation of the blood is observed in two dimensions.

FIGS. 12 to 17 illustrate a second embodiment for which the elementssimilar to the first embodiment previously described are identifiedusing identical references, and are not described again.

According to the second embodiment, the characterization system 10 isdesigned more particularly to characterize the agglomeration ofparticles contained in a liquid 12. The characterization system 10 isfor example capable of characterizing the agglomeration of bloodparticles, such as red blood cells, also called agglutination of bloodparticles.

Information relative to the blood group is then also determined from theagglomeration state, also called agglutination state.

As known per se, the blood group can be determined using theBeth-Vincent test by detecting the presence of A or B antigens implyingthe absence of anti-A or anti-B antibodies. In the case where theerythrocytes of the tested blood have an A or B antigen, anantigen-antibody complex will be formed and lead to a cellularaggregation as recalled in table 200, shown in FIG. 12.

The fluid chamber 14 includes two separate circulation channels 202,204, i.e., a first channel 202 and a second channel 204, as shown inFIG. 13.

According to the second embodiment, the light source 16 is any type oflight source. The laser 16 is not necessarily spatially and temporallycoherent.

According to the second embodiment, the second calculation software 38is able to calculate the second indicator Ind2 capable of characterizingthe agglomeration of the particles, the second indicator Ind2 being anintensity indicator for each acquired image I_(n)(x,y). The secondindicator Ind2 is representative of the histogram of the intensity ofeach pixel in the image I_(n), or in a region of interest thereof. It isdetermined for example by measuring the total intensity of the imageI_(n) or in a predetermined region of interest of the image I_(n),optionally after thresholding.

The characterization software 40 is next capable of determining anagglomeration state of the particles of the liquid 12 from the secondcalculated indicator Ind2. The agglomeration state is for exampledetermined when the second indicator Ind2 exceeds a predeterminedthreshold.

In the described embodiment, where the liquid 12 contains blood, theparticles are for example red blood cells, and the characterizationsoftware is then capable of determining information relative to theblood group from the agglomeration state.

The operation of the second embodiment will now be described using FIGS.13 to 17.

During the initial step 100, the liquid 12, for example the blood sampleof a donor whereof one wishes to determine the blood group, isintroduced into the deposition area 26 of the fluid chamber. The liquid12 then flows from the deposition area 26 toward the circulationchannels 202, 204, for example by capillarity.

The liquid 12 is then mixed with first 206 and second 208 distinctreagents, during step 110, as shown in FIG. 13.

Each reagent 206, 208 is for example deposited upstream from the opticaldetection area corresponding to the area inside the dotted lines in FIG.13 for which an image is acquired by the photodetector 20.

The mixture between the liquid 12 and the first and second reagents 206,208 is done when the liquid 12 flows into contact with the first reagent206 inside the first channel 202 (arrow F2), and respectively the secondreagent 208 inside the second channel 204 (arrow F3).

In the described embodiment, the first reagent 206 is a donor A serum,i.e., containing anti-B antibodies, and the second reagent 208 is adonor B serum, i.e., containing anti-A antibodies.

Depending on the blood group associated with the blood sample 12, acellular aggregation will or will not then occur in each of thecirculation channels 202, 204.

The liquid 12, such as the blood sample mixed with the first and secondreagents 206, 208, is then lighted by the light beam 18 during step 120.

During step 130, the matrix photodetector 20 then acquires atransmission image I(x,y) corresponding to an optical detection areaencompassing the two circulation channels 202, 204.

One skilled in the art will observe that, according to the secondembodiment, the acquisition of a single transmission image I(x,y) makesit possible to characterize the agglomeration of the particles containedin the liquid 12, by comparing that image with a reference imageI_(ref)(x,y), the latter for example being an image made over areference area, not shown, in which the blood is not mixed with thereagent. This reference area is for example a third channel, with ageometry identical to that of the first or second channel 202, 204, andnot including any reagent.

Alternatively, this is an area situated on the first channel 202 or onthe second channel 204, upstream from the reagent 206, 208.

Alternatively, the reference image I_(ref)(x,y) is produced at the samelocation as the transmission image, just after filling of the channel bythe analyzed liquid, the transmission image I(x,y) being done, under thesame conditions, after certain time, for example 1 minute, such that anyeffect of the reagent on the analyzed liquid is measurable.

The acquired image I(x,y) corresponds similarly to the diffraction andthe diffusion of the light beam 18 by the particles suspended in aliquid 12. Preferably, this image is done under identical conditions forthe two channels, as well as for the reference area. For identicalconditions, we particularly refer to the lighting conditions, thesource-detector distance, the characteristics of the detector used, theplacement time, the observed field, the size of the image.

The lighting of the particles by the laser beam 18 creates a diffractionpattern. As previously indicated, the absence of a magnification lensbetween the fluid and the photodetector 20, coupled with the significantsurface of the incident beam, makes it possible to form a usable imageat a short distance, covering a large fluid field, such as a fieldhaving an area of several millimeters squared.

During the acquisition step 130, the photodetector 20 is placed near thefluid chamber 14, the second distance D2 between the fluid chamber 14and the photodetector 20 in the longitudinal direction X being smallerthan 1 cm.

In the example embodiment of FIG. 14, showing an acquired image 210 ofthe first channel 202, and respectively an acquired image 212 of thesecond channel 204, the cellular aggregation is observed only in thesecond channel 204 through the presence of white stains in the image212. In other words, the blood group associated with the tested bloodsample is group B according to the table 200 of FIG. 12.

At the end of the acquisition step 130, the second calculation software38 calculates, during step 140, the second indicator Ind2 able tocharacterize the agglomeration of the particles, the second indicatorInd2 being an intensity indicator for each acquired image I(x,y). Thesecond indicator Ind2 is representative of the intensity in thepredetermined region of interest of the image, in particular thedistribution of the intensity of the pixels in said region.

The second indicator Ind2 is, for example, a characteristic of theimage, and in particular of histogram of the gray level of the imageacquired for each channel 202, 204, and, if applicable, the referencechannel as illustrated in FIGS. 16 and 17, showing a histogram 214 ofthe gray level of the acquired image of the first channel 202, andrespectively a histogram 216 of the gray level of the acquired image ofthe second channel 204. Each gray level histogram 214, 216 has the graylevel values on the x-axis and the pixel population on the y-axis, i.e.,the number of pixels, for a gray level given on the x-axis. Acharacteristic of the histogram is for example the mean intensity of thepixels, denoted I_(mean), after thresholding of the image, thatthresholding making it possible to keep only the information for thepixels whereof the intensity is above a certain threshold.

Referring to the example shown in FIGS. 16 and 17, after havingperformed thresholding at the intensity corresponding to the value 120,it is understood that the mean intensity of the image corresponding toFIG. 17 is higher than the mean intensity of the image corresponding toFIG. 16. This is due to the fact that the histogram of FIG. 17, showingthe observation of an agglutination of particles, comprises more intensepixels (gray levels higher than 200) than the histogram of FIG. 16, thelatter showing the observation of a non-agglutination. The secondindicator Ind2 is then for example established according to the meanintensity of the image.

According to one alternative, one determines, for each produced image,the intensity I_(max), the latter corresponding, on the histogram of theimage, to the highest value of the intensity bringing together apredetermined number of pixels, for example 500 pixels. One thendetermines the deviation between I_(max) and I_(mean), by subtractingI_(max)−I_(mean), the second indicator Ind2 then representing thatdeviation. On the histogram of FIG. 17, the second indicator Ind2 thusdefined is higher than in the histogram of FIG. 16. The value of thesecond indicator Ind2 thus determined makes it possible to conclude onthe presence or absence of an agglutination phenomenon through acomparison, for example with a value Ind2_(ref) obtained on a referencearea, or with a predetermined value, the predetermination of that valuefor example being done according to experimental tests.

According to one alternative, on each transmission image, the intensityI_(peak) corresponding to the maximum value of the histogram isdetermined, i.e., the intensity value bringing together the highestnumber of pixels. In FIGS. 16 and 17, this value corresponds to the peakof each distribution, respectively equal to 120 and 130. One alsodetermines the maximum value I_(max) bringing together a number ofpixels with a value above a predetermined threshold. In reference toFIGS. 16 and 17, and by adopting a threshold of 500, I_(max) isrespectively equal to 181 and 256. The second indicator Ind2 correspondsto the distance between I_(max) and I_(peak), 61 for FIGS. 16 and 126for FIG. 17, respectively. One concludes on an agglutination when thesecond indicator Ind2 is higher, for example by 25%, than a certainpredetermined threshold, or when it is higher than the indicatorInd2_(ref) established for the reference area.

According to one alternative, the second indicator Ind2 is a comparisonindicator between a region of interest of the transmission image I and areference image I_(ref) not containing reagent (and therefore in whichthe agglutination does not occur), as below:

${{Ind}\; 2} = \frac{\sum\limits_{x}^{\;}\; {\sum\limits_{y}^{\;}\; {{{I\left( {x,y} \right)} - {I_{ref}\left( {x,y} \right)}}}}}{{\sum\limits_{x}^{\;}\; {\sum\limits_{y}^{\;}{I\left( {x,y} \right)}}} + {I_{ref}\left( {x,y} \right)}}$

The second indicator Ind2 is compared with a predetermined threshold,for example 0.25. Thus, if the second indicator Ind2 is above thatthreshold, an agglutination is found.

The characterization software 40 is capable of then determining anagglomeration state of the particles of the liquid 12 from the secondcalculated indicator Ind2.

The agglomeration state is for example determined when the secondindicator Ind2 exceeds a predetermined threshold.

If the comparison is positive, i.e., if the gray level obtained isgreater than the predetermined threshold, then the characterizationsoftware 40 deduces the presence of a cellular aggregation in thecorresponding channel 202, 204.

In the second described embodiment, the characterization software 40lastly determines the blood group associated with the blood sample 12tested from the type of the first and second regions 206, 208, as wellas from the table 200.

The advantages of this second embodiment are identical to those of thefirst embodiment previously described.

As a complement to the first embodiment, the fluid chamber 14 includes aplurality of channels, for example the two channels 202, 204 visible inFIG. 13, so as to characterize the variation of the speed of theparticles of the liquid 12 when the liquid 12 is mixed with differentreagents, a respective reagent then being positioned in each channel202, 204 of the fluid chamber 14. This makes it possible, with a samedevice, to determine different analysis parameters of the same liquidsample, for example the coagulation time and the blood group.

Such a fluid chamber 14 is for example advantageous to characterize thecoagulation of the liquid 12 containing blood, with different reagentscapable of favoring the slowing of the blood particles via a coagulationof the blood, such as the different reagents 112 defined above.

One can see that the characterization system 10 according to theinvention makes it possible to observe a larger part of the fluidchamber 14, while having a limited bulk.

FIGS. 18 to 29 illustrate a second example of the second embodiment, inwhich the liquid to be characterized 12 is a biological liquid, inparticular blood or diluted blood, and the j characterization system 10is capable of characterizing the agglomeration of particles, in thiscase red blood cells, in the biological liquid 12.

The liquid to be characterized 12 in this example includes blood dilutedat 1/20 in a PBS (Phosphate Buffered Saline) buffer, the bufferincluding 1% by volume of FBS (Fetal Bovine Serum).

The volume of diluted blood is 40 μL, to which a variable quantity ofantibodies is added, such as an anti-red blood cell called CD235A, forexample marketed by the company Becton Dickinson under reference BD555569. The quantity of antibodies added varies from 0 to 1 μg ofantibodies per μl of undiluted blood, which corresponds to aconcentration comprised between 0 and 6.7 μM.

The addition of these antibodies makes it possible to mask the surfaceantigens of the red blood cells (in particular glycophorin A), whichcauses their agglutination.

The aim of this second example is to show that it is possible tocharacterize an agglutination state of blood particles, for example redblood cells, by lenseless imaging using the characterization system 10.

For each quantity of antibodies added into the liquid to becharacterized 12, the liquid sample to be characterized 12 is acquiredusing the characterization system 10, i.e., by lenseless imaging, theobtained images 220A, 220B, 220C and 220D being visible in FIGS. 18 to21. A grayscale histogram of the intensity of the pixels of each ofthese images 220A, 220B, 220C and 220D is then calculated, thecalculated histograms 222A, 222B, 222C and 222D being visible in FIGS.22 to 25. Reference images 224A, 224B, 224C and 224D of the liquidsample to be characterized 12 are also obtained with a microscope, asshown in FIGS. 26 to 29. It should be noted that, for microscopicobservation, the blood sample is diluted with a dilution factor of 1/10.

In the second example, the light source 16 is a laser diode, having anemission spectrum centered on a wavelength λ for example equal to 670nm, and the first distance D1 is substantially equal to 8 cm. The sampleis confined in the fluid chamber 14 including a channel 28 with athickness of 150 μm formed between two transparent walls with athickness of 200 μm. These walls are made from a plastic material, forexample from COP (Cyclo Olefin Polymer).

The fluid chamber 14 is directly placed on the glass cover of the matrixphotodetector 20, such as a CMOS sensor, including 1280*1024 pixels,each pixel having size 5 μm×5 μm, such that the fluid chamber 14 ispositioned between the CMOS sensor and the light source 16. The seconddistance D2 is then preferably smaller than 1 cm, for example equal to550 μm.

The image acquisitions are for example done with an exposure time of 5ms, with one image per acquisition. The images 220A, 220B, 220C and 220Drespectively correspond to an increasing added quantity of antibodies.More specifically, the images 220A, 220B, 220C and 220D respectivelycorrespond to:

-   -   a substantially zero quantity of antibodies,    -   a quantity of antibodies below a threshold concentration C,    -   a quantity of antibodies equal to the threshold concentration C,        and    -   a quantity of antibodies equal to 2 times the threshold        concentration C.

When the quantity of added antibodies exceeds the thresholdconcentration C, the red blood cells agglomerate and the images obtainedby lenseless imaging using the characterization system 10 reflect thesize of the agglutinates. The value of the threshold concentration C isfor example equal to 250 ng of antibodies for 1 μl of undiluted blood,which corresponds to 1.7 μM.

It will be observed that the agglomeration of red blood cells causes theappearance of extended light areas (high gray level) delimited by darkareas (low gray level). This image segmentation effect into areasincluding several tens, or hundreds of pixels, of comparable graylevels, may be observed by comparing the images 220A (FIG. 18) or 220B(FIG. 19), in which no agglutination is observed, with the images 220C(FIG. 20) and 220D (FIG. 21), in which agglutination is observed. Thepresence or absence of an agglutination of particles observable in theimages 220A, 220B, 220C and 220D is confirmed by the microscopeobservations shown in the images 224A (FIG. 26), 224B (FIG. 27), 224C(FIG. 28) and 224D (FIG. 29), respectively. This results in an evolutionof the histogram of each image, the latter tending to stretch toward thelow gray level values as the quantity of agglomerates increases, asshown from the histogram 222A corresponding to the image 220A toward thehistogram 222D corresponding to the image 220D.

The agglutination state of the blood sample is then quantified bycalculating the second indicator Ind2 according to several possiblealternatives:

-   -   according to a first alternative, the second indicator Ind2 is        equal to the standard deviation of the intensity distribution of        the pixels of the examined area of interest, and is then denoted        Ind2_(A),    -   according to a second alternative, the second indicator Ind2 is        equal to the number of pixels below a certain threshold, that        threshold for example being a fraction of the maximum gray        level, divided by the total number of pixels in the examined        area of interest, and the second indicator Ind2 calculated        according to this second alternative is then denoted Ind2_(B).        In the example of FIGS. 22 to 25, the threshold value is equal        to 125.

Table 1 below shows the value of the second indicator Ind2 according tothese two alternatives and for each of the areas of interest shown inFIGS. 18 to 21.

FIGS. 18 19 20 21 Ind2_(A) 36 33 51 59 Ind2_(B) 6.5 10⁻³ 6.5 10⁻³ 8.710⁻² 1.4 10⁻¹

When the second indicator Ind2_(A) according to the first alternative isbelow a threshold value, for example comprised between 40 and 45, thereis no observable agglutination. Beyond that threshold value, the higherthe value of the second indicator Ind2_(A), the greater the quantity ofagglutinated particles.

The second indicator Ind2_(B), calculated according to the secondalternative, makes it possible to reach the same conclusions, taking athreshold value comprised between 1 10⁻² and 5 10⁻².

One can see that it is possible to observe, or even quantify, anagglutination state of particles in the biological liquid 12, using anindicator calculated from an image obtained by the characterizationsystem 10, i.e., by lenseless imaging, and in particular the secondindicator Ind2_(A), Ind2_(B), according to the first and secondalternatives of this example, said second indicator depending on thedistribution of the intensity of the pixels of the images 220A, 220B,220C and 220D acquired by the characterization system 10.

The characterization system 10 can also be used in a diagnostic testbased on the detection of agglutinates in a biological fluid.

FIGS. 30 to 45 illustrate a third example of the second embodiment, inwhich the liquid to be characterized 12 is a biological liquid, inparticular blood or diluted blood, and the characterization system 10 iscapable of characterizing the agglomeration, also called agglutination,of particles in the biological liquid 12.

In this third example, the detection of the agglutination of red bloodcells in a blood sample is shown, including a variable quantity of Aprotein, the agglutination being caused by the addition of a givenquantity of a reagent (an antibody).

The liquid to be characterized 12 for example includes blood diluted at1/20 in a PBS (Phosphate Buffered Saline) buffer, the buffer including1% by volume of FBS (Fetal Bovine Serum).

The volume of diluted blood is 40 μL, to which an antibody is incubated,such as an anti-red blood cell called CD235A, for example marketed bythe company Becton Dickinson under reference BD 555569, with an Aprotein solution in a variable quantity. The incubation duration is 1hour.

Thus, there are several so-called antibody—A protein solutionsavailable, in which the antibody—A protein molar ratio is variable. Thesolutions may cause the agglutination of red blood cells, resulting inthe name “pro-equipment solutions”. A volume of 1.2 μL of each of thesesolutions is incubated with 40 μL of diluted blood sample describedabove, for 1.5 hours, each of these mixtures forming a liquid sample tobe characterized 12.

In each of the mixtures thus obtained, the S antibody molarconcentration is below the threshold C determined in the previous secondexample. In other words, this antibody concentration does not allow thespontaneous agglutination of red blood cells. In the case at hand, thisconcentration S is 100 ng of antibodies per μl of undiluted blood, i.e.,0.7 μM.

For each of the liquid samples to be characterized 12, an imageacquisition is done using the characterization system 10, i.e., bylenseless imaging, the acquired images 230A, 230B, 230C, 230D and 230Eobtained being shown in FIGS. 30 to 34. A grayscale histogram of theintensity of the pixels of each of these images 230A, 230B, 230C, 230Dand 230E is then calculated, the calculated histograms 232A, 232B, 232C,232D and 232E being shown in FIGS. 35 to 39. Reference images 234A,234B, 234C, 234D and 234E of each of the liquid samples to becharacterized 12 are also obtained with a microscope, as shown in FIGS.40 to 44. It should be noted that, for microscopic observation, theblood sample is diluted with a dilution factor of 1/10.

In this third example, the light source 16 is a laser diode, having anemission spectrum centered on a wavelength λ equal to 670 nm, and thefirst distance D1 is substantially equal to 8 cm. The sample is confinedin the fluid chamber 14 including a channel 28 with a thickness of 150μm formed between two transparent walls with a thickness of 200 μm.These walls are made from a plastic material, for example COP (CycloOlefin Polymer).

The fluid chamber 14 is directly placed on the glass cover of the matrixphotodetector 20, such as a CMOS sensor. The CMOS sensor for example hasa matrix of 1280 by 1024 pixels, each pixel being in the shape of asquare, each side measuring 5 μm, such that the fluid chamber 14 ispositioned between the CMOS sensor and the light source 16. The seconddistance D2 is then preferably smaller than 1 cm, for example equal to550 μm.

The image acquisitions are for example done with an exposure time of 5ms, with one image per acquisition. The images 230A, 230B, 230C, 230Dand 230E respectively correspond to an increasing added quantity of Aprotein. More specifically, the images 230A, 230B, 230C, 230D and 230Erespectively correspond to:

-   -   absence of antibodies, i.e., an Antibody:A Protein molar        ratio=0:40 (0 antibody molecules for 40 A protein molecules),    -   absence of A protein, i.e., an Antibody—A Protein molar        ratio=1:0 (1 antibody molecule for 0 A protein molecules),    -   Antibody:A Protein molar ratio=1:1 (1 antibody molecule for 1 A        protein molecule),    -   Antibody:A Protein molar ratio=1:5 (1 antibody molecule for 5 A        protein molecules), and    -   Antibody:A Protein molar ratio=1:40 (1 antibody molecule for 40        A protein molecules).

The antibody here serves as a bonding agent between an A proteinmolecule and a red blood cell, as will be outlined later.

In the presence of A protein and in the absence of antibodies, no redblood cell agglutination is observed, as shown in FIG. 30. In thepresence of antibodies and the absence of A protein, no red blood cellagglutination is observed, which is shown in FIG. 31.

When the antibody:A protein ratio is equal to 1:1, there is also noobserved red blood 0 cell agglutination, as shown in FIG. 32.

When the antibody:A protein ratio is equal to 1:5, red blood cellagglutination is observed, shown in FIG. 33. When the antibody:A proteinratio is equal to 1:40, red blood cell agglutination is also observed,shown in FIG. 34, the size of the agglutinates observed in FIG. 34 beinglarger than that of the agglutinates observed in FIG. 33.

FIGS. 35 to 39 show the histogram of the intensity of the pixels ofFIGS. 30 to 34, respectively.

One can see that the agglomeration of red blood cells causes theappearance of light areas (high gray level) delimited by a dark area(low gray level). This image segmentation effect into areas includingseveral tens, or hundreds of pixels, of comparable gray levels, may beobserved by comparing the images 230A (FIG. 30) or 230B (FIG. 31) or230C (FIG. 32), in which no agglutination is observed, with images 230D(FIG. 33) and 230E (FIG. 34), in which agglutination is observed. Thepresence or absence of an agglutination of particles observable in theimages 230A, 230B, 230C, 230D and 230E is confirmed by the microscopeobservations shown in the images 234A (FIG. 40), 234B (FIG. 41), 234C(FIG. 42), 234D (FIG. 43) and 234E (FIG. 44), respectively. This resultsin an evolution of the histogram of each image, the latter tending tostretch toward the low gray level values as the quantity of agglomeratesincreases, as shown from the histogram 232A corresponding to the image230A toward the histogram 232E corresponding to the image 230E. Theagglutination state of the blood sample is then quantified bycalculating the second indicator Ind2 according to several possiblealternatives:

-   -   according to a first alternative, the second indicator Ind2 is        equal to the standard deviation of the intensity distribution of        the pixels of the examined area of interest, and is then denoted        Ind2_(A),    -   according to a second alternative, the second indicator Ind2 is        equal to the number of pixels below a certain threshold, that        threshold for example being a fraction of the maximum gray        level, divided by the total number of pixels in the examined        area of interest, and the second indicator Ind2 calculated        according to this second alternative is then denoted Ind2_(B).        In the example of FIGS. 22 to 25, the threshold value is equal        to 125.

Table 2 below shows the value of the second indicator Ind2 according tothese two alternatives and for each of the areas of interest shown inFIGS. 30 to 34.

FIGS. 30 31 32 33 34 Ind2_(A) 33 39 35 50 52 Ind2_(B) 4.0 10⁻³ 1.5 10⁻²5.5 10⁻³ 6.5 10⁻² 9.5 10⁻²

When the second indicator Ind2_(A) according to the first alternative isbelow a threshold value, for example comprised between 40 and 45, thereis no observable agglutination. Beyond that threshold value, the higherthe value of the second indicator Ind2_(A), the greater the quantity ofagglutinated particles.

The second indicator Ind2_(B), calculated according to the secondalternative, makes it possible to arrive at the same conclusions, usinga threshold value comprised between 1 10⁻² and 5 10⁻².

One can thus see that it is possible to observe, or even quantify, anagglutination state of particles in the biological liquid 12, using anindicator calculated from an image obtained by the characterizationsystem 10, i.e., by lenseless imaging, and in particular the secondindicator Ind2_(A), Ind2_(B), according to the first and secondalternatives of this example, which depends on the distribution of theintensity of the pixels of the images 230A, 230B, 230C, 230D and 230Eacquired by the characterization system 10.

Furthermore, the greater the quantity of A protein, the larger the sizeof the agglutinates, the quantity of antibodies added being constant.Thus, the second indicator Ind2_(A), Ind2_(B) quantifying theagglutination state may also quantify a quantity of protein in the bloodsample.

Depending on the antibody-A protein molar ratio, the red blood cellsagglomerate and the images 230A, 230B, 230C, 230D and 230E obtained bylenseless imaging reflect the size of the agglutinates, i.e., the degreeof agglutination. It is then understood that by introducing apredetermined quantity of antibodies into the blood sample, it ispossible to estimate the quantity of A protein present in that sampleaccording to the agglutination state, i.e., according to the secondindicator Ind2_(A), Ind2_(B) previously described.

In other words, the quantity of A protein beyond which agglutination isobserved constitutes the detection limit for assaying that protein in ablood sample, by introducing a given quantity of antibodies into theliquid sample to be characterized 12.

Thus, one will understand that it is possible to observe, or evenquantify, an agglutination state of particles in a biological fluid,using indicators relative to the image obtained by lenseless imaging,and in particular the second indicator Ind2_(A), Ind2_(B), according tothe first and second alternatives of this second example, which dependson the distribution of the intensity of the pixels. This agglutinationstate for example depends on the concentration of an analyte in thebiological liquid, the quantification of that agglutination state thenmaking it possible to assay that analyte in the liquid. The exampleshows that that assay can be done by introducing a bifunctional reagent,in this case the antibody 300, into a blood sample, capable of bondingboth on a particle of the biological fluid, in this case the red bloodcells 302, and on the analyte to be assayed, in this case the A protein304, thereby forming a bridge between an analyte 304 and the red bloodcells 302, as shown in FIG. 45.

The term bifunctional designates the ability of the reagent to bond bothon a particle and an analyte.

In general, the term “analyte” refers to a chemical or biologicalspecies present in the liquid, such as a molecule, a macromolecule (forexample, protein or nucleic acid), a cell, a bacteria, a virus, orspore.

Furthermore, the analyte 304 must include at least two bonding siteswith the bifunctional reagent, as shown in FIG. 45. Thus, each analyte304 may be bonded, via the bifunctional reagent, to at least twoparticles. This causes agglutination of the particles.

In other words, the agglutination state of the particles in the liquid12 depends on the quantity of analyte present in the liquid 12, thatquantity being able to be assayed by adding a reagent capable of causingthe formation of agglutinates, the reagent 300 then being able to bondbetween one of said particles 302 and an analyte 304 so as to form anagglutinate.

As a function of the quantity of analyte 304 present in the liquid, anagglutinate is formed, made up of particles 302 and analytes 304. Bydetermining the agglutination state corresponding to a given quantity ofintroduced reagent, it is then possible to estimate the quantity ofanalyte 304 present in the liquid 12.

In the second and third examples of the second embodiment, previouslydescribed, the second distance D2 is smaller than 1 cm. The inventorsnevertheless also observed that, in the case of the characterization ofagglutination, values of the second distance D2 greater than 1 cm, suchas values of several centimeters, or even several tens of centimeters,do make it possible to obtain usable results, although values of thesecond distance D2 below 1 cm remain preferable.

In general, these second and third examples demonstrate another aspectof the invention. According to this other aspect, the invention relatesto a method for characterizing the agglutination of particles, such asbiological particles, in a liquid, for example a biological liquid, andin particular a bodily fluid, the characterization method including thefollowing steps:

-   -   introducing liquid into a fluid chamber,    -   lighting the fluid chamber using a light beam, the light beam in        particular coming from a light source, such as a laser diode or        a light-emitting diode,    -   acquiring an image, or a plurality of images, of the fluid        chamber using a matrix photodetector, the photodetector        preferably being placed at a distance from the fluid chamber of        less than 1 cm, the fluid chamber being positioned between the        light source and the matrix photodetector.    -   processing the image, or the plurality of images, to determine        an indicator characterizing the agglutination of particles in        the biological liquid, and    -   characterizing the agglutination of particles in the liquid,        depending on the value of the indicator.

It should be noted that the image is acquired by the photodetector,preferably without a magnifying lens between the fluid chamber and thematrix photodetector. However, objective microlenses may be provided ateach pixel of the detector, as previously stated.

Additionally and optionally, the indicator is an indicator representingthe distribution of the intensity of the pixels in an image, or moregenerally, any other indicator translating the segmentation of the imageinto different areas, each area including several tens to hundreds ofpixels of comparable intensity, i.e., where the intensity is distributedin a range of gray levels of approximately half, or even one third, oreven one quarter, or even less than one quarter of the dynamic of theimage.

Additionally and optionally, the characterization method includes theaddition of a reagent, which can cause the agglutination of particles inthe liquid.

As illustrated in the third example of the second embodiment,agglutination of the particles for example depends on a quantity ofanalyte present in the liquid.

According to this alternative, the invention relates to a method fordetecting the quantity of an analyte in a liquid, for example abiological liquid, and in particular a bodily fluid, the detectionmethod including the following steps:

-   -   introducing liquid into a fluid chamber,    -   lighting the fluid chamber using a light beam, the light beam in        particular coming from a light source, such as a laser diode or        a light-emitting diode,    -   adding a reagent, capable of causing the formation of        agglutinates of particles and analytes in the liquid,    -   acquiring an image, or a plurality of images, of the fluid        chamber using a matrix photodetector, the photodetector        preferably being placed at a distance from the fluid chamber of        less than 1 cm, the fluid chamber being positioned between the        light source and the matrix photodetector,    -   processing the image, or the plurality of images, to determine        an indicator characterizing the agglutination of particles in        the biological liquid, and    -   estimating the quantity of analyte in the liquid, as a function        of the value of the indicator.

According to still another aspect, the invention relates to a method fordetermining a parameter of the liquid 12, including blood, the methodincluding the following steps:

-   -   introducing the liquid 12 in the fluid chamber 14,    -   lighting the fluid chamber 14 using the excitation laser beam 18        emitted by the light source 16, the laser beam 18 extending        through the fluid chamber 14 in the longitudinal direction X,    -   acquiring at least one image I_(n)(x,y), I_(n+m)(x,y), I(x,y)        using the matrix photodetector 20, the image I_(n)(x,y),        I_(n+m)(x,y), I(x,y) being formed by radiation transmitted by        the lighted fluid chamber 14, and

determining an indicator Ind1_(n,n+m), Ind2, from said at least oneimage I_(n)(x,y), I_(n+m)(x,y), I(x,y).

During the acquisition step, the photodetector 20 is positioned at thedistance D2, smaller than 1 cm, from the fluid chamber 14 in thelongitudinal direction X.

As a complement and optionally, the determination method comprises oneor more of the following features, considered alone or according to alltechnically possible combinations:

-   -   the light beam 18 directly lights the fluid chamber 14, and the        image I_(n)(x,y), I_(n+m)(x,y), I(x,y) is formed directly by the        radiation transmitted by the lighted fluid chamber 14, in the        absence of a magnification lens positioned between the fluid        chamber 14 and the photodetector 20;    -   the parameter is a coagulation, and the method then includes the        following-steps:        -   mixing the liquid 12 with a reagent to favor the coagulation            of the blood,        -   acquiring a series of transmission images I_(n)(x,y),            I_(n+m)(x,y) at different moments n, n+m,        -   calculating an indicator Ind1_(n,n+m) to establish a            correlation between two areas of the transmission images            I_(n)(x,y), I_(n+m)(x,y),

the coagulation being determined as a function of the value of saidindicator;

-   -   the parameter is a coagulation time, and the method then        includes the following steps:        -   mixing the liquid 12 with a reagent to favor the coagulation            of the blood,        -   acquiring a series of transmission images I_(n)(x,y),            I_(n+m)(x,y) at different moments n, n+m,        -   calculating an indicator Ind1_(n,n+m) to establish a            correlation between two images I_(n)(x,y), I_(n+m)(x,y), and        -   determining a time interval, called coagulation time,            between an initial moment t0 and the moment t2_(A), t2_(B)            at which the indicator Ind1_(n,n+m) takes a predetermined            value.    -   The parameter is an agglutination of blood particles, and the        method then includes the following steps:        -   mixing the liquid 12 with a reagent capable of creating an            agglutination of the blood particles,        -   acquiring a transmission image I(x,y),        -   calculating an indicator Ind2 as a function of the intensity            in a predetermined area of the transmission image I(x,y),            and        -   determining an agglutination state when that indicator Ind2            exceeds a predetermined threshold;    -   the blood particles are red blood cells, the reagent including        an antibody, the agglutination state then providing information        relative to the blood group.

According to this other independent aspect, the invention also relatesto a system for determining a parameter of the liquid 12, includingblood, the determination system comprising:

-   -   the fluid chamber 14 designed to receive the liquid 12;    -   the light source 16 capable of emitting the excitation laser        beam 18 to light the fluid chamber 14, the laser beam 18        extending the longitudinal direction X;    -   the matrix photodetector 20 capable of acquiring at least one        image I_(n)(x,y), I_(n+1)(x,y), I(x,y) of a radiation        transmitted by the lighted fluid chamber 14; and    -   the information processing unit 21 including means for        determining an indicator Ind1_(n,n+m), Ind2, from said at least        one image I_(n)(x,y), I_(n+m)(x,y), I(x,y).

The photodetector 20 is positioned at the distance D2, smaller than 1cm, from the fluid chamber 14 in the longitudinal direction X.

The parameter is a coagulation, coagulation time, or an agglutination ofblood particles.

1. A method for characterizing a variation in the speed of particles oragglomeration of particles, the particles, such as blood particles,being contained in a liquid (12), the method including the followingsteps: introducing (100) the liquid (12) into a fluid chamber (14);lighting (120) the fluid chamber (14) using an excitation laser beam(18) emitted by a light source (16), the laser beam (18) extendingthrough the fluid chamber (14) in a longitudinal direction (X);acquiring (130) at least one image (I_(n)(x,y), I_(n+m)(x,y); I(x,y))using a matrix photodetector (20), the image (I_(n)(x,y), I_(n+m)(x,y);I(x,y)) being formed by radiation transmitted by the lighted fluidchamber (14); and calculating (140), from at least one acquired image(I_(n)(x,y), I_(n+m)(x,y); I(x,y)), at least one indicator(Ind1_(n,n+m), Ind2) characterizing the variation of the speed oragglomeration of the particles; characterized in that, during theacquisition step (130), the photodetector (20) is positioned at adistance (D2) smaller than 1 cm from the fluid chamber (14) in thelongitudinal direction (X).
 2. The method according to claim 1, whereinthe laser beam (18) has a surface area comprised between 5 mm² and 200mm², preferably equal to 25 mm², in a plane (P) perpendicular to thelongitudinal direction (X), said plane (P) being arranged in contactwith the fluid chamber (14).
 3. The method according to claim 1, whereinthe laser beam (18) directly lights the fluid chamber (14), and theimage (I_(n)(x,y), I_(n+m)(x,y); I(x,y)) is formed directly by theradiation transmitted by the lighted fluid chamber (14), in the absenceof a magnifying lens positioned between the fluid chamber (14) and thephotodetector (20).
 4. The method according to claim 1, wherein, duringthe acquisition step (130), several transmission images (I_(n)(x,y),I_(n+m)(x,y)) are acquired sequentially at different moments (n, n+m),and wherein, during the calculation step (140), a first calculatedindicator (Ind1_(n,n+m)) is a correlation indicator able to characterizethe variation of the speed of the particles, the correlation indicator(Ind1_(n,n+m)) being representative of the correlation between at leasttwo transmission images (I_(n)(x,y), I_(n+m)(x,y)), respectivelyacquired at moments n and n+m, or the correlation for a predeterminedregion of said transmission images (I_(n)(x,y), I_(n+m)(x,y)).
 5. Themethod according to claim 4, wherein the method also includes a step(110) for mixing the liquid (12) with a reagent (112) capable offavoring slowing of the particles, such as a reagent (112) capable offavoring slowing of the blood particles by means of coagulation of theblood.
 6. The method according to claim 4, wherein the method alsoincludes a step for determining, from the first calculated indicator(Ind1_(n,n+m)), the coagulation of blood particles and/or a timeinterval (Tc), called coagulation time, between an initial moment (t0)and the moment (t2_(A), t2_(B)) at which the first calculated indicator(Ind1_(n,n+m)) takes a predetermined value.
 7. The method according toclaim 4, wherein the light source (16) is a spatially and temporallycoherent light source, such as a laser.
 8. The method according to claim4, wherein the first indicator (Ind1_(n,n+m)) is calculated from acorrelation image (Icorr_(n,n+m)(x,y)) defining the spatial correlationbetween said transmission images (I_(n), I_(n+m)), or from apredetermined region (142) of said correlation image(Icorr_(n,n+m)(x,y)).
 9. The method according to claim 8, wherein thecorrelation image (Icorr_(n,n+m)(x,y)) is determined by the followingequation:${{Icorr}_{n + m}\left( {x,y} \right)} = \frac{\left( {{\left( {A_{n} \times A_{n + m}} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}{\sqrt{\left( {{\left( A_{n}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}\sqrt{\left( {{\left( A_{n + m}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}}$where x and y represent the coordinates of a point of the image,Icorr_(n+m)(x,y) is a matrix having X rows and Y columns, k1(x,y)represents a predetermined matrix having P rows and Q columns,A_(n)(x,y) and A_(n+m)(x,y) are defined by the following equations:A _(n)(x,y)=I _(n)(x,y)−(I _(n)

k1)(x,y)A _(n+m)(x,y)=I _(n+m)(x,y)−(I _(n+m)

k1)(x,y) I_(n)(x,y), I_(n+m)(x,y) representing two successivetransmission images at moments n and n+m, I_(n)(x,y), I_(n+m)(x,y) beingmatrices with X rows and Y columns, and the symbol

represents the convolution integer defined by:${\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\; {\sum\limits_{q = 0}^{Q}\; {{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}$F(x,y) being a matrix with X rows and Y columns, X, Y, P and Q beingintegers verifying X≧P≧1 and Y≧Q≧1.
 10. The method according to claim 4,wherein the first indicator (Ind1_(n,n+m)) is calculated using thefollowing equation:${{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\; {C_{n + m}^{2}\left( {x,y} \right)}}}}$where Ind1_(n,n+m) represents the first indicator, C_(n)(x,y) andC_(n+m)(x,y) are defined by the following equations:C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n)C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m) I′_(n)(x,y), I′_(n+m)(x,y)respectively represent a predetermined region of two successivetransmission images at moments n and n+m, x and y designating thecoordinates of a point of the image, I′_(n)(x,y), I′_(n+m)(x,y) beingmatrices having N rows and M columns, and I′_(n) , I′_(n+m) representinga mean value of respective predetermined regions I′_(n)(x,y) andI′_(n+m)(x,y).
 11. The method according to claim 1, wherein the methodalso includes a step (110) for mixing the liquid (12) with a reagent(206, 208) capable of creating an agglomeration of particles, in which,during the calculation step (110), a second calculated indicator (Ind2)is an indicator for each acquired image (I(x,y)), the second indicator(Ind2) being representative of the intensity of the pixels of the image(l(x,y)) in a predetermined region of the image (I(x,y)), and whereinthe method also includes a step for determining an agglomeration stateof the particles from the second calculated indicator (Ind2).
 12. Themethod according to claim 11, wherein the agglomeration state isdetermined when the second indicator (Ind2) exceeds a predeterminedthreshold.
 13. The method according to claim 11, wherein theagglomeration state is determined when the second indicator (Ind2)exceeds a reference indicator (Ind2_(ref)), obtained by an image made inthe reference area.
 14. The method according to claim 11, wherein theblood particles are red blood cells, the reagent (206, 208) includes anantibody, and a piece of information relative to the blood group is alsodetermined from the agglomeration state.
 15. The method according toclaim 11, wherein the liquid (12) includes an analyte, the method thenincluding estimating the quantity of said analyte in the liquid (12), asa function of said second indicator (Ind2).
 16. The method according toclaim 1, wherein the fluid chamber (14) includes several fluidcirculation channels (202, 204), and wherein, during the calculationstep (140), an indicator (Ind1_(n,n+m), Ind2) is calculated for each ofthe channels (202, 204).
 17. A system (10) for characterizing thevariation of the speed of particles or the agglomeration of particles,the particles, such as blood particles, being contained in the liquid(12), the system (10) comprising: a fluid chamber (14) designed toreceive the liquid (12); a light source (16) capable of emitting anexcitation laser beam (18) to light the fluid chamber (14), the laserbeam (18) extending through the fluid chamber (14) in a longitudinaldirection (X); a matrix photodetector (20) capable of acquiring at leastone image (I_(n)(x,y), I_(n+1)(x,y); I(x,y)), of a radiation transmittedby the lighted fluid chamber (14); and an information processing unit(21) including calculation means (36, 38) for calculating, from at leastone acquired image (I_(n)(x,y), I_(n+1)(x,y); I(x,y)), at least oneindicator (Ind1_(n,n+m), Ind2) characterizing the variation of the speedor the agglomeration of the particles; characterized in that thephotodetector (20) is positioned at a distance (D2) smaller than 1 cmfrom the fluid chamber in the longitudinal direction (X).
 18. The system(10) according to claim 17, wherein the matrix photodetector (20)includes a plurality of pixels, each pixel having dimensions eachsmaller than or equal to 4 μm.